Rethinking Climate Econometrics: Data Cleaning, Flexible Trend Controls, and Predictive Validation

TL;DR

This paper critically evaluates climate econometric models using modern statistical learning, highlighting issues with traditional methods and identifying more robust predictors through advanced validation techniques.

Contribution

It introduces robust data preprocessing, nonparametric trend controls, and out-of-sample validation to improve climate econometric analysis.

Findings

Most traditional predictors have limited predictive power.

Humidity-related variables are more consistent predictors.

Current models' empirical foundations are challenged by new analysis.

Abstract

We assess empirical models in climate econometrics using modern statistical learning techniques. Existing approaches are prone to outliers, ignore sample dependencies, and lack principled model selection. To address these issues, we implement robust preprocessing, nonparametric time-trend controls, and out-of-sample validation across 700+ climate variables. Our analysis reveals that widely used models and predictors-such as mean temperature-have little predictive power. A previously overlooked humidity-related variable emerges as the most consistent predictor, though even its performance remains limited. These findings challenge the empirical foundations of climate econometrics and point toward a more robust, data-driven path forward.